Abstract: The study of Arctic sea ice thickness is of significant importance for understanding global climate change and exploring Arctic shipping routes. While satellite remote sensing and numerical simulation techniques have been widely employed in sea ice thickness studies, there are significant spatiotemporal discrepancies among various sea ice thickness data products, unlike the research on sea ice concentration. Therefore, this paper proposes a comprehensive quality assessment framework for sea ice thickness data products to objectively and quantitatively evaluate their accuracy and applicability. The framework extracts digital statistical features, local spatial distributions, and temporal variation pattern of different sea ice thickness products from 2010 to 2020, constructing nine evaluation indicators. Through comparative analysis with observed data, multidimensional quantitative evaluation of sea ice thickness products is achieved. The results indicate that: (1) CryoSat-2 and SMOS (CS2SMOS) products excel in five indicators, including statistical feature correlation, spatial structure similarity, interannual variation deviation, monthly change correlation, and monthly change deviation; (2) PIOMAS product best reflects the temporal characteristics of sea ice thickness during the winter half-year and exhibits optimal interannual variation correlation; (3) CPOM product performs best in three indicators, including feature statistical deviation, spatial distribution correlation, and spatial distribution deviation. The research findings can be used for the fusion of sea ice thickness data products, enabling the objective and reliable weighting of different sea ice thickness data products in different spatiotemporal contexts, thereby enhancing the objectivity and reliability of sea ice thickness data product fusion.

Key words: multi-source data, sea ice thickness, assessment, Arctic